[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121987-id":3,"doc-seo-121987-113":31,"detail-sidebar-cat-0-id-113":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},121987,2336474466712,"Maeve","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",54,"Penelitian & Laporan","Penggunaan Machine Learning Algoritma Support Vector Machine (SVM) untuk Mengidentifikasi Kadar Pasir Besi - di Kabupaten Aceh Besar","Penelitian ini membahas penerapan machine learning untuk kegiatan eksplorasi, khususnya mengidentifikasi keterdapatan dan kadar pasir besi di Kabupaten Aceh Besar. Fokus kajian diarahkan pada algoritma Support Vector Machine (SVM) yang berperan dalam klasifikasi dan regresi dengan menemukan hyperplane terbaik ber-margin maksimal. Model SVM dibangun menggunakan sejumlah parameter pendukung, meliputi jarak titik sampel terhadap pantai, sungai, dan sesar, nilai pixel, data ketinggian, temperatur, curah hujan, serta jenis batuan penyusun. Evaluasi menunjukkan hubungan kadar Fe prediksi dan aktual yang kuat dengan RMSE 0,076 dan r2 0,705.","Penggunaan Machine Learning Algoritma Support Vector Machine (SVM) untuk Mengidentifikasi Kadar Pasir Besi  \ndi Kabupaten Aceh Besar  \nMuhammad Rizki Kana 1, *, Nadhiratur Rahmi 1, Mulkal 1  \n1 Program Studi Teknik Pertambangan, Fakultas Teknik, Universitas Syiah Kuala, Banda Aceh, Indonesia.  \n* Corresponding author: [rizki820@mhs.usk.ac.id](rizki820@mhs.usk.ac.id)[ ](rizki820@mhs.usk.ac.id)[Received: May 5](Received: May 5), [2024](2024); Accepted: Jun 1, 2024 DOI: [doi.org/10.31764/jpl.v5i1.23216](doi.org/10.31764/jpl.v5i1.23216)  \nAbstrak. Seiring dengan perkembangan zaman, teknologi eksplorasi berkembang dengan sangat pesat. Salah satunya ialah penerapan machine learning dalam kegiatan eksplorasi. Penggunaan machine learning memungkinkan untuk mendapatkan sebaran mineral pasir besi pada lokasi tertentu dengan menambahkan berbagai parameter yang berpengaruh sehingga mendapatkan output berupa keputusan terkait keterdapatan dan kadar mineral pasir besi pada daerah tersebut. Dalam hal ini, penelitian yang dilakukan hanya berfokuspada algoritma Support Vector Machine (SVM) . SVM merupakan salah satu algoritma machine learning yang digunakan untuk tugas-tugas klasifikasi dan regresi. Oleh karena itu, penelitian ini dilakukan untukmendapatkan model algoritma SVM yang dapat digunakan untuk mengidentifikasi kadar pasir besi dengan menambah beberapa parameter pendukung seperti data jarak titik sampel terhadap pantai, jarak titik sampel terhadap sungai, jarak titik sampel terhadap sesar, nilai pixel, data ketinggian, temperatur, data curah hujan, dan jenis batuan penyusun. Hasilnya grafik regresi linear menunjukkan hubungan nilai kadar Fe prediksi dari model SVM dan kadar Fe aktual, dimana nilai Root Mean Square Error (RMSE) adalah 0,076 dan nilai r2 adalah 0,705 . Artinya nilai model algoritma SVM yang dibuat memiliki tingkat kesalahan yang kecil dankorelasi antar data yang kuat sehingga algoritma tersebut dapat dijalankan untuk mengidentifikasi kadar pasir besi.  \nKata Kunci: teknologi eksplorasi, SVM, machine learning, pasir besi.  \nAbstract. Along with the times, exploration technology is developing very rapidly. One of them is the application of machine learning in exploration activities. The use of machine learning makes it possible to obtain the distribution of iron sand minerals in a particular location by adding various influential parameters so as to get output in the form of decisions related to the presence and level of iron sand minerals in the area. In this case, the research conducted only focuses on the Support Vector Machine (SVM) algorithm. SVM is one of the machine learning algorithms used for classification and regression tasks. Therefore, this research was conducted to obtain an SVM algorithm model that can be used to identify iron sand levels by adding several supporting parameters such as data on the distance of the sample point to the beach, the distance of the sample point to the river, the distance of the sample point to the fault, the pixel value, altitude data, temperature, rainfall data, and the type of constituent rock. The result is a linear regression graph showing the relationship between the predicted Fe content value of the SVM model and the actual Fe content, where the Root Mean Square Error (RMSE) value is 0.076 and the r2 value is 0.705. This means that the value of the SVM algorithm model created has a small error rate and a strong correlation between data so that the algorithm can be run to identify iron sand levels.  \nKeywords: exploration technology, SVM, machine learning, iron sand.  \n1. Pendahuluan  \nDalam tahapan kegiatan pertambangan, untuk mengidentifikasi keberadaan endapan bahan galianterutama pasir besi, perlu dilakukan proses ekplorasi terlebih dahulu. Kegiatan ekplorasi terdiri darikegiatan eksplorasi langsung dan kegiatan eksplorasi tidak langsung. Kegiatan ekplorasi langsung  \nini merupakan metode eksplorasi yang dilakukan penggalian langsung pada endapan bahan galianyang akan di","cbCaicLIFytGrRpt","https://ap.wps.com/l/cbCaicLIFytGrRpt","pdf",615594,3,1,9,"Indonesian","id",113,"# Pendahuluan\n## Konteks eksplorasi pasir besi\n## Peran machine learning dalam eksplorasi\n## Alasan penggunaan algoritma SVM\n## Tujuan penelitian","[{\"question\":\"Mengapa eksplorasi tidak langsung penting dalam identifikasi endapan pasir besi?\",\"answer\":\"Eksplorasi tidak langsung dilakukan untuk mengurangi risiko dan biaya tinggi yang biasanya melekat pada eksplorasi langsung. Pengamatan memanfaatkan teknologi agar lebih efisien waktu dan operasional.\"},{\"question\":\"Bagaimana SVM digunakan untuk mengidentifikasi kadar pasir besi?\",\"answer\":\"SVM dipakai untuk tugas klasifikasi dan regresi dengan cara mencari hyperplane terbaik sebagai pemisah antar kelas ber-margin maksimal. Model kemudian memprediksi kadar Fe berdasarkan parameter masukan.\"},{\"question\":\"Apa hasil kinerja model SVM pada prediksi kadar Fe?\",\"answer\":\"Hasil regresi linear menunjukkan hubungan kuat antara kadar Fe prediksi dan kadar Fe aktual, dengan RMSE sebesar 0,076 dan r2 sebesar 0,705. Nilai ini mengindikasikan tingkat kesalahan kecil serta korelasi data yang kuat.\"}]","Penggunaan Machine Learning Algoritma Support Vector Machine (SVM) untuk Mengidentifikasi Kadar Pasir Besi - di Kabupaten Aceh Besar | PDF",1785808164,14,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"using-machine-learning-support-vector-machine-svm-to-identify-iron-sand-levels-in-aceh-besar-regency","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/id/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/id/document/penelitian-laporan/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/id/document/using-machine-learning-support-vector-machine-svm-to-identify-iron-sand-levels-in-aceh-besar-regency/121987/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-15","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Mengapa eksplorasi tidak langsung penting dalam identifikasi endapan pasir besi?","Question",{"text":76,"@type":77},"Eksplorasi tidak langsung dilakukan untuk mengurangi risiko dan biaya tinggi yang biasanya melekat pada eksplorasi langsung. Pengamatan memanfaatkan teknologi agar lebih efisien waktu dan operasional.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Bagaimana SVM digunakan untuk mengidentifikasi kadar pasir besi?",{"text":81,"@type":77},"SVM dipakai untuk tugas klasifikasi dan regresi dengan cara mencari hyperplane terbaik sebagai pemisah antar kelas ber-margin maksimal. Model kemudian memprediksi kadar Fe berdasarkan parameter masukan.",{"name":83,"@type":74,"acceptedAnswer":84},"Apa hasil kinerja model SVM pada prediksi kadar Fe?",{"text":85,"@type":77},"Hasil regresi linear menunjukkan hubungan kuat antara kadar Fe prediksi dan kadar Fe aktual, dengan RMSE sebesar 0,076 dan r2 sebesar 0,705. Nilai ini mengindikasikan tingkat kesalahan kecil serta korelasi data yang kuat.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,99,103,107,111,115,117,121,125,129,133],{"id":95,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":100,"doc_module":4,"doc_module_name":47,"category_name":101,"show_sort_weight":97,"slug":102},48,"Cerita & Novel","story-novel",{"id":104,"doc_module":4,"doc_module_name":47,"category_name":105,"show_sort_weight":97,"slug":106},56,"Gaya Hidup","lifestyle",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":97,"slug":110},51,"Komik","comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":97,"slug":114},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":97,"slug":116},"research-report",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":119,"show_sort_weight":97,"slug":120},49,"Sastra","literature",{"id":122,"doc_module":4,"doc_module_name":47,"category_name":123,"show_sort_weight":97,"slug":124},52,"Teknologi","technology",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":127,"show_sort_weight":97,"slug":128},50,"Ujian","exam",{"id":130,"doc_module":4,"doc_module_name":47,"category_name":131,"show_sort_weight":97,"slug":132},57,"Umum","general",{"id":134,"doc_module":4,"doc_module_name":47,"category_name":135,"show_sort_weight":4,"slug":136},181,"Formulir","formulir"]